Visible to the public Detection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture

TitleDetection of Phishing Attacks with Machine Learning Techniques in Cognitive Security Architecture
Publication TypeConference Paper
Year of Publication2019
AuthorsOrtiz Garcés, Ivan, Cazares, Maria Fernada, Andrade, Roberto Omar
Conference Name2019 International Conference on Computational Science and Computational Intelligence (CSCI)
Date Publisheddec
Keywordsartificial intelligence, Artificial neural networks, Big Data, cognitive security application, cognitive security architecture, composability, compositionality, Computational Intelligence, Computer crime, cryptography, cybersecurity, cybersecurity analysts, Data analysis, data analytics, Internet, learning (artificial intelligence), machine learning, machine learning algorithms, machine learning techniques, phishing, phishing web attacks detection, pubcrawl, python, python tools, Uniform resource locators, URL analysis
AbstractThe number of phishing attacks has increased in Latin America, exceeding the operational skills of cybersecurity analysts. The cognitive security application proposes the use of bigdata, machine learning, and data analytics to improve response times in attack detection. This paper presents an investigation about the analysis of anomalous behavior related with phishing web attacks and how machine learning techniques can be an option to face the problem. This analysis is made with the use of an contaminated data sets, and python tools for developing machine learning for detect phishing attacks through of the analysis of URLs to determinate if are good or bad URLs in base of specific characteristics of the URLs, with the goal of provide realtime information for take proactive decisions that minimize the impact of an attack.
Citation Keyortiz_garces_detection_2019